State-level hierarchical generalized additive model (GAM) that models the prevalence of RR-TB positive cases per quarter among incident TB cases between 2014-2019
Fit smoothing functions to reduce the noise we were seeing in previous models
Models risk of positivity by characteristics of patient and municipality where they reside
Separate models for new cases and previously treated cases (e.g. relapse and re-entry)
result ~ s(state, bs = "re") + s(time) + s(time, by = state, id = 1) + age_cat + hiv_status + sex + health_unit + bf_cat + urban_cat + has_prison + fhs_cat
Random intercept for each state (patient state of residence)
A different smooth function for time by state with a shared smoothing parameter
Each state-level smoothing parameter varies around a grand smooth function for time to allow for pooling across states
Fixed effects for patient-level characteristics:
Fixed effects for characteristics of region where patient resides (either municipality or micro-region level):
Model 1 - 2014-2019
Model 2 - 2016-2019
Run separately by case type (new vs. previously treated (relapse, re-entry))
A. Municipality-level
B. Microregion-level
A. Municipality-level
B. Microregion-level
C. Municipality and Micro-region